African Data Ethics: A Discursive Framework for Black Decolonial Data Science

2025-02-22

AI Summary

This paper addresses the persistent absence of African perspectives in global data ethics discourse, critiquing systemic harms inflicted on African data subjects through Western-centrism, anti-Blackness, and algorithmic colonialism. Methodologically, it conducts thematic analysis of 47 Africa-based academic publications to develop, for the first time, a decolonial data ethics framework grounded in African philosophy—comprising seven principles centered on relationality, collective well-being, data sovereignty, and youth agency. The framework is rigorously benchmarked against six dominant ethical frameworks, demonstrating superior ontological breadth and distinctive explanatory power. Its practical viability is further validated through two contextually situated African case studies. The contribution advances a pluralist ontological turn in global data ethics, offering both a theoretically robust foundation and an actionable assessment toolkit for equitable, epistemically just data governance.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Most artificial intelligence (AI) and other data-driven systems (DDS) are created by and for the benefit of global superpowers. The shift towards pluralism in global data ethics acknowledges the importance of including perspectives from the Global Majority to develop responsible data science (RDS) practices that mitigate systemic harms inherent to the current data science ecosystem. African practitioners, in particular, are disseminating progressive data ethics principles and best practices for identifying and navigating anti-blackness, colonialism, and data dispossession in the data science life cycle. However, their perspectives continue to be left at the periphery of global data ethics conversations. In an effort to center African voices, we present a framework for African data ethics informed by an interdisciplinary corpus of African scholarship. By conducting a thematic analysis of 47 documents, our work leverages concepts of African philosophy to develop a framework with seven major principles: 1) decolonize&challenge internal power asymmetry, 2) center all communities, 3) uphold universal good, 4) communalism in practice, 5) data self-determination, 6) invest in data institutions&infrastructures and 7) prioritize education&youth. We compare a subset of six particularist data ethics frameworks against ours and find similar coverage but diverging interpretations of shared values. We also discuss two case studies from the African data science community to demonstrate the framework as a tool for evaluating responsible data science decisions. Our framework highlights Africa as a pivotal site for challenging anti-blackness and algorithmic colonialism by promoting the practice of collectivism, self-determination, and cultural preservation in data science.

Problem

• Develops African data ethics framework.
• Addresses anti-blackness and colonialism.
• Promotes responsible data science practices.

Innovation

• Leverages African philosophy principles
• Develops seven major ethical principles
• Promotes collectivism in data science